Understanding Artificial Intelligence
Despite their broad potential, generative AI models also have several important limitations. Understanding these limitations is critical for using these technologies ethically and effectively.
Ethical Concerns
Quality and Reliability
Data Privacy and Security
Energy Consumption and Environmental Impact
Human Dependency, De-skilling, and Displacement
--adapted from https://libguides.rutgers.edu/artificial-intelligence
Generating content like this can be done efficiently using a large language model, but it is important to remember to review the output carefully and acknowledge the source.
Artificial Intelligence (AI)
Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.
Types of Artificial Intelligence
A chatbot is a software application that uses natural language processing (NLP) and machine learning to simulate conversation with humans, either via text or voice interfaces.
Generative artificial intelligence refers to algorithms and models that can generate new content or data, such as images, videos, music, or text, based on patterns learned from existing information.
Machine learning is a subset of artificial intelligence that involves training computer systems to learn from data and improve their performance over time through experience.
NLP is a subfield of artificial intelligence that deals with the interaction between computers and human language, including text and speech processing, sentiment analysis, machine translation, and dialogue systems.
A large language model is a type of machine learning model that is trained on vast amounts of text data to generate language outputs that are coherent and contextually appropriate.
Large Language Models (LLMs)
In the context of AI, hallucination refers to the phenomenon where a model generates inaccurate or imaginary output that cannot be explained by its training data, often due to overfitting or underfitting.
A prompt is a specific task or question that is given to an AI system to elicit a response or output.
Prompt engineering is the process of designing and refining prompts to elicit desired responses or behaviors from AI systems, in order to improve their performance and versatility.
Understanding Large Language Models (LLMs)
Parameters are settings or values that are adjusted during the training process to optimize the performance of an AI model, such as the learning rate, regularization strength, or number of hidden layers.
In Natural Language Processing and machine learning, tokens refer to individual words or phrases in a text dataset, which are used as input features for models to analyze and understand the meaning of the text.
Training data is the set of examples or inputs used to train an AI system, which helps the model learn patterns and relationships in the data and make predictions or decisions.
[1] Tim Miller (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267, 1-38.
[2] Ming-Hui Huang & Roland Rust (2018). Artificial Intelligence in Service. Journal of Service Research, 21 (2), pp. 155-172.
[3] Andreas Kaplan & Michael Haenlein (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62 (1), pp. 15-25.
[4] Yanqing Duan , John Edwards , & Professor Yogesh K Dwivedi (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, pp. 63-71.
[5] Professor Yogesh K Dwivedi , Dr Laurie Hughes , Elvira Ismagilova , Gert Aarts , Crispin Coombs , Tom Crick MBE , Yanqing Duan , Rohita Dwivedi, PhD , John Edwards , Aled Eirug , Vassilis Galanos , P. Vigneswara Ilavarasan. , Marijn Janssen , Paul Jones , Arpan K. Kar , Dr. Hatice Kizgin , Bianca Kronemann , Lal, B., Biagio Lucini , Rony Medaglia , Kenneth Le Meunier-FitzHugh , Le Meunier-FitzHugh, L.C., Santosh M. , Emmanuel Mogaji, Professor Sujeet K. Sharma , Singh, J.B., Vishnupriya Raghavan , Dr Ramakrishnan Raman , Nripendra P. Rana, PhD, SFHEA , Spyridon Samothrakis , Jak Spencer Kuttimani Tamilmani , Annie Tubadji , Paul Walton , & Michael Williams (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, art. no. 101994,
[6] Spyros Makridakis (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, pp. 46-60.
[7] Bench-Capon, T.J.M., Dunne, P.E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171 (10-15), pp. 619-641.
[8] Michael Haenlein and Andreas Kaplan (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61 (4), pp. 5-14.
[9] Tom Davenport , Abhijit Guha , Dhruv Grewal & Timna Bressgott (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48 (1), pp. 24-42.
[10] Mohammad Hossein Jarrahi (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61 (4), pp. 577-586.